#!/bin/bash

#当前路径,不需要修改
cur_path=`pwd`
#集合通信参数,不需要修改
export RANK_SIZE=1
export JOB_ID=10087
RANK_ID_START=0
# 数据集路径,保持为空,不需要修改
data_path=""
#设置默认日志级别,不需要修改
export ASCEND_GLOBAL_LOG_LEVEL=3
#基础参数，需要模型审视修改
#网络名称，同目录名称
Network="Speech_Transformer_ID0487_for_PyTorch"
#训练epoch
train_epochs=2
#训练batch_size
batch_size=32
#训练step
train_steps=3
max_steps=10
#学习率
learning_rate=0.001
#TF2.X独有，不需要修改
export NPU_LOOP_SIZE=${train_steps}
#维测参数，precision_mode需要模型审视修改
precision_mode=
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False
autotune=False
bin_mode=False
bin_analysis=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_full_1p.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		         if or not over detection, default is False
    --data_dump_flag	     data dump flag, default is False
    --data_dump_step		 data dump step, default is 10
    --profiling		         if or not profiling for performance debug, default is False
    --autotune               whether to enable autotune, default is False
    --data_path		         source data of training
    -h/--help		         show help message
    "
    exit 1
fi
#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --autotune* ]];then
        autotune=`echo ${para#*=}`
        mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
        mv $install_path/fwkacllib/data/tiling/Ascend910/custom $install_path/fwkacllib/data/tiling/Ascend910/custom_bak
        autotune_dump_path=${cur_path}/output/autotune_dump
        mkdir -p ${autotune_dump_path}/GA
        mkdir -p ${autotune_dump_path}/rl
        cp -rf $install_path/fwkacllib/data/tiling/Ascend910/custom ${autotune_dump_path}/GA/
        cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/RL/
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --bin_mode* ]];then
        bin_mode="True"
    elif [[ $para == --bin_analysis* ]];then
        bin_analysis="True"
    fi
done
#校验是否传入data_path,不需要修改
#if [[ $data_path == "" ]];then
#    echo "[Error] para \"data_path\" must be confing"
#    exit 1
#fi
#修改模糊编译写法
if [ $bin_mode == "True" ];then
    step_line=`grep "torch.npu.set_start_fuzz_compile_step(3)" ${cur_path}/../train.py -n | awk -F ':' '{print $1}'`
    sed -i "${step_line}s/^/#/" ${cur_path}/../train.py
    inc_line=`grep "torch.npu.global_step_inc()" ${cur_path}/../train.py -n | awk -F ':' '{print $1}'`
    sed -i "${inc_line}s/^/#/" ${cur_path}/../train.py
    sed -i "43itorch.npu.global_step_inc()" ${cur_path}/../train.py
fi
#设置二进制变量
if [ $bin_analysis == "True" ];then
    #增加编译缓存设置
    line=`grep "    main()" ${cur_path}/../train.py -n | awk -F ':' '{print $1}'`
    sed -i "${line}itorch.npu.set_option(option)" ${cur_path}/../train.py
    sed -i "${line}s/^/    /" ${cur_path}/../train.py
    sed -i "${line}ioption['ACL_OP_COMPILER_CACHE_MODE'] = 'disable'" ${cur_path}/../train.py
    sed -i "${line}s/^/    /" ${cur_path}/../train.py
    sed -i "${line}ioption = {}" ${cur_path}/../train.py
    sed -i "${line}s/^/    /" ${cur_path}/../train.py
fi
#训练开始时间，不需要修改
start_time=$(date +%s)
#进入训练脚本目录，需要模型审视修改
cd $cur_path/../

sed -i "s|aishell_folder = 'data/data_aishell'|aishell_folder = '$data_path/data/data_aishell'|g" $cur_path/../config.py
sed -i "s|pickle_file = 'data/aishell.pickle'|pickle_file = '$data_path/data/aishell.pickle'|g" $cur_path/../config.py

for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $ASCEND_DEVICE_ID"
    export RANK_ID=$RANK_ID
    #创建DeviceID输出目录，不需要修改
    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    fi
    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
    python3 pre_process.py

    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune
    python3 train.py --epochs=2 --max_steps $max_steps > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
done
wait
#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

sed -i "s|aishell_folder = '$data_path/data/data_aishell'|aishell_folder = 'data/data_aishell'|g" $cur_path/../config.py
sed -i "s|pickle_file = '$data_path/data/aishell.pickle'|pickle_file = 'data/aishell.pickle'|g" $cur_path/../config.py

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep Epoch  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F "Throughput" '{print$2}' | awk '{print$1}' | awk '{sum+=$1} END {print"",sum/NR}'`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"
#输出训练精度,需要模型审视修改
train_accuracy=`grep Time $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $7}'`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"
#稳定性精度看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
if [ $bin_mode == "True" ];then
    CaseName=$CaseName"_binary"
fi
#二进制支持算子
if [ $bin_analysis == "True" ];then
    cmd1=`ls -l /usr/local/Ascend/CANN-1.82/opp/op_impl/built-in/ai_core/tbe/kernel/config/ascend910|grep -v total|awk -F " " '{print $9}'|awk -F "." '{print $1}'`
    echo "cmd1=$cmd1" >> ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log
fi
##获取性能数据
#吞吐量，不需要修改
ActualFPS=${FPS}
#单迭代训练时长，不需要修改
TrainingTime=`${batch_size}'/'${FPS}`
#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep Epoch $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "Loss" '{print$2}' | awk '{print$1}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
#最后一个迭代loss值，不需要修改
ActualLoss=${train_accuracy}
#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log

